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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

557
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
557
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

274
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
274
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

249
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
249
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

535
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
535
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

329
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
329
Modeling with Differential Equations01:25

Modeling with Differential Equations

80
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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A Data-Driven Approach to Quantifying Immune States in Sepsis
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Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model.

Yuanyuan Zhang1, Shudong Wang2, Xinzeng Wang3

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China.

Biomed Research International
|December 25, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-driven method for identifying differentially methylated regions (DMRs) in DNA methylation data. The approach accounts for data characteristics, improving sensitivity and accuracy in disease mechanism research.

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • DNA methylation is crucial for gene regulation and altered in disease.
  • Identifying differentially methylated regions (DMRs) aids in understanding complex disease mechanisms.
  • Existing methods lack comprehensive integration of DNA methylation data characteristics.

Purpose of the Study:

  • To develop a novel data-driven approach for identifying DMRs.
  • To account for the correlation of neighboring CpG sites and data heterogeneity.
  • To improve the accuracy and sensitivity of DMR identification.

Main Methods:

  • A data-driven approach using a modified 1D Ising model to evaluate single-site energy.
  • Incorporation of neighboring CpG site correlations and data heterogeneity.
  • Comparison with existing methods (DMRcate, ProbeLasso, Wang's) on simulated and real datasets.

Main Results:

  • The proposed method demonstrates higher sensitivity in simulated data compared to competing methods.
  • Applied to real data, it identifies more common DMRs with a high overlap ratio.
  • The necessity of integrating heterogeneity and correlation characteristics for DMR identification is confirmed.
  • Approximately 90% of identified DMRs are located in CpG islands (CGIs), suggesting a role in gene expression regulation.

Conclusions:

  • The novel method effectively identifies DMRs by integrating key characteristics of DNA methylation data.
  • This approach enhances the understanding of DNA methylation's functional role in disease.
  • The findings highlight the importance of considering data heterogeneity and CpG site correlations for accurate DMR detection.